Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

doi:10.4251/wjgo.v16.i3.857

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

World J Gastrointest Oncol. Mar 15, 2024; 16(3): 857-874
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.857

Table 1 Characteristic baseline of patients in sets

Variables	Total set (n = 190)	Training set (n = 106)	Internal test set (n = 47)	External test set (n = 37)	P value
VETC (%)					0.986
Positive	94 (49)	53 (50)	23 (49)	18 (49)
Negative	96 (51)	53 (50)	24 (51)	19 (51)
Age (median, IQR)	57 (51, 66)	58.5 (51, 65.75)	56 (51.5, 67.5)	57 (50, 64)	0.708
Sex (%)					0.555
Male	153 (81)	83 (78)	38 (81)	32 (86)
Female	37 (19)	23 (22)	9 (19)	5 (14)
Hepatitis (%)					0.799
HBV or/and HCV	171 (90)	96 (91)	41 (87)	34 (92)
Negative	19 (10)	10 (9)	6 (13)	3 (8)
Cirrhosis (%)					0.008
Present	158 (83)	85 (80)	36 (77)	37 (100)
Absent	32 (17)	21 (20)	11 (23)	0 (0)
ALT (median, IQR)	32 (19, 51.75)	28 (18, 49)	40 (22.5, 63)	33 (22, 45)	0.094
AST (median, IQR)	37 (25, 61.5)	36.5 (23.25, 1.75)	49 (28.5, 78)	30 (24, 46)	0.041
GGT (median, IQR)	59.25 (31, 131.25)	61.75 (30, 127)	76 (38.5, 143.5)	49 (27, 98)	0.157
AFP (median, IQR)	49.06 (5.54, 9.25)	62.74 (6.62, 9.25)	56.78 (5.87, 16.5)	34.74 (5.22, 798)	0.516
Main tumor size (median, IQR)	5.7 (3.2, 9.28)	6.05 (3.02, 9.3)	6.9 (3.45, 11.15)	4.11 (3.2, 6.5)	0.098
Multiplicity (%)					0.037
≥ 2	46 (24)	29 (27)	14 (30)	3 (8)
1	144 (76)	77 (73)	33 (70)	34 (92)
Single lobe involvement (%)					0.064
Present	141 (74)	74 (70)	34 (72)	33 (89)
Absent	49 (26)	32 (30)	13 (28)	4 (11)
Intratumor hemorrhage (%)					0.179
Present	12 (6)	9 (8)	3 (6)	0 (0)
Absent	178 (94)	97 (92)	44 (94)	37 (100)
Intratumor necrosis (%)					0.131
Present	95 (50)	57 (54)	25 (53)	13 (35)
Absent	95 (50)	49 (46)	22 (47)	24 (65)
Arterial phase hyper enhancement (%)				0.701
Present	179 (94)	101 (95)	44 (94)	34 (92)
Absent	11 (6)	5 (5)	3 (6)	3 (8)
Well defined capsule (%)					0.143
Present	140 (74)	75 (71)	33 (70)	32 (86)
Absent	50 (26)	31 (29)	14 (30)	5 (14)
Washout (%)					0.249
Present	187 (98)	105 (99)	45 (96)	37 (100)
Absent	3 (2)	1 (1)	2 (4)	0 (0)
Non-smooth tumor margin (%)					0.435
Present	116 (61)	69 (65)	26 (55)	21 (57)
Absent	74 (39)	37 (35)	21 (45)	16 (43)

VETC: Vessels encapsulating tumor cluster; IQR: Interquartile range; HBV: Hepatitis B virus; HCV: Hepatitis C virus; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: Gamma-glutamyl transferase; AFP: Alpha-fetoprotein.

Table 2 Univariable and Multivariable logistic regression for upstaging in the training set

Variables	VETC- (n = 53)	VETC+ (n = 53)	Univariate analysis		Multivariate analysis
Variables	VETC- (n = 53)	VETC+ (n = 53)	OR	P value	OR	P value
Age, median (IQR)	63 (50, 67)	55 (51, 62)	0.997	0.096
Sex (%)			1.344	0.637
Male	40 (75)	43 (81)
Female	13 (25)	10 (19)
Hepatitis (%)			1.133	0.74
HBV or/and HCV	47 (89)	49 (92)
Negative	6 (11)	4 (8)
Cirrhosis (%)			0.669	0.626
Present	44 (83)	41 (77)
Absent	9 (17)	12 (23)
ALT, median (IQR)	23 (15, 38)	36 (20, 52)	1.010	0.011	1.001	0.738
AST, median (IQR)	29 (21, 50)	40 (28, 68)	0.989	0.022	0.991	0.450
GGT, median (IQR)	39 (27, 87)	100 (40, 185)	1.000	0.001	1.000	0.209
AFP, median (IQR)	62.23 (5.48, 446.4)	78.51 (8.05, 8213)	0.999	0.076
Main tumor size, median (IQR)	4.1 (2.4, 6.5)	8.9 (5.6, 10.8)	2.815	< 0.001	1.873	< 0.001
Multiplicity (%)			0.799	0.009	0.907	0.660
≥ 2	8 (15)	21 (40)
1	45 (85)	32 (60)
Single lobe involvement (%)			0.620	< 0.001	0.952	0.617
Present	46 (87)	28 (53)
Absent	7 (13)	25 (47)
Intratumor hemorrhage (%)			0.609	1
Present	4 (8)	5 (9)
Absent	49 (92)	48 (91)
Intratumor necrosis (%)			0.850	< 0.001	7.947	< 0.001
Present	13 (25)	44 (83)
Absent	40 (75)	9 (17)
Arterial phase hyperenhancement (%)			1.112	0.363
Present	49 (92)	52 (98)
Absent	4 (8)	1 (2)
Well defined capsule (%)			1.018	1
Present	38 (72)	37 (70)
Absent	15 (28)	16 (30)
Washout (%)			1.815	1
Present	52 (98)	53 (100)
Absent	1 (2)	0 (0)
Non-smooth tumor margin (%)			1.717	0.014	1.109	0.881
Present	28 (53)	41 (77)
Absent	25 (47)	12 (23)

Table 3 Selected radiomics features in intratumoral, peritumoral, and combined radiomics models on the training set

Intratumoral radiomics model	Peritumoral radiomics model	Combined radiomics model
Original_GLDM_DependenceEntropy	Original_shape_Sphericity	Original_GLDM_DependenceEntropy¹
Lbp-3D-k_GLRLM_ShortRunHighGrayLevelEmphasis	Lbp-2D_firstorder_InterquartileRange	Original_GLRLM_RunLengthNonUniformity¹
Lbp-3D-k_GLDM_SmallDependenceEmphasis	Lbp-3D-k_firstorder_Minimum	Wavelet-HHH_GLCM_SumEntropy¹
Original_GLRLM_RunLengthNonUniformity	Wavelet-HHH_GLCM_SumEntropy	Wavelet-HHH_GLCM_SumEntropy²
Wavelet-HHH_GLCM_SumEntropy	Original_GLRLM_RunVariance	Original_GLRLM_RunVariance²
Lbp-3D-k_firstorder_Kurtosis	Wavelet-LHL_firstorder_Variance	Logarithm_firstorder_InterquartileRange¹
Wavelet-HLH_GLRLM_GrayLevelNonUniformityNormalized	Lbp-3D-m1_firstorder_Skewness	Wavelet-LHL_firstorder_Variance²
Squareroot_firstorder_Minimum	Logarithm_firstorder_10Percentile	Wavelet-HHH_GLCM_MCC¹
Wavelet-LLH_GLCM_Imc2	Squareroot_firstorder_10Percentile	Lbp-3D-k_firstorder_Kurtosis¹
Wavelet-LHL_GLCM_MaximumProbability	Wavelet-HLL_firstorder_Kurtosis	Wavelet-HHH_firstorder_Kurtosis²
Wavelet-HLH_GLCM_MaximumProbability		Lbp-3D-m1_firstorder_Skewness²
		Wavelet-HLH_GLRLM_GrayLevelNonUniformityNormalized_V1¹
		Logarithm_firstorder_90Percentile²

¹Intratumoral radiomics.

²Peritumoral radiomics.

GLRLM: Gray level run length matrix; GLCM: Gray level co-occurrence matrix; GLDM: Gray level dependence matrix; Lbp-2D: Local binary pattern in 2D; Lbp-3D: Local binary pattern in 3D.

Table 4 Performance of logistic regression, support vector machine, decision tree, and random forest in the combined radiomics for predicting vessels encapsulating tumor clusters

Set	ML model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	LR	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
	SVM	0.874 (0.805-0.943)	0.764	0.792	0.736	0.745	0.765
	DT	0.862 (0.794-0.930)	0.820	0.811	0.830	0.827	0.815
	RF	1 (1.000-1.000)	1	1	1	1	1
Internal test
	LR	0.788 (0.649-0.927)	0.745	0.783	0.708	0.720	0.773
	SVM	0.766 (0.629-0.903)	0.681	0.739	0.625	0.654	0.714
	DT	0.698 (0.556-0.840)	0.659	0.696	0.625	0.640	0.682
	RF	0.723 (0.577-0.869)	0.702	0.739	0.667	0.667	0.696
External test
	LR	0.680 (0.498-0.862)	0.676	0.500	0.842	0.750	0.640
	SVM	0.632 (0.438-0.826)	0.676	0.500	0.842	0.75	0.640
	DT	0.667 (0.482-0.852)	0.676	0.500	0.842	0.750	0.640
	RF	0.614 (0.428-0.800)	0.568	0.444	0.684	0.571	0.565

LR: Logistic regression; SVM: Support vector machine; DT: Decision tree; RF: Random forest; CI: Confidence interval; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Table 5 Performance evaluation of the logistic regression models on the training set and the two test sets

Set	Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	Intratumoral radiomics	0.772 (0.684-0.860)	0.689	0.736	0.642	0.673	0.708
	Peritumoral radiomics	0.823 (0.745-0.901)	0.745	0.774	0.717	0.732	0.760
	Combined radiomics	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
Internal test
	Intratumoral radiomics	0.768 (0.628-0.908)	0.638	0.696	0.583	0.615	0.667
	Peritumoral radiomics	0.757 (0.615-0.899)	0.702	0.783	0.625	0.750	0.667
	Combined radiomics	0.788 (0.649-0.927)	0.745	0.783	0.708	0.720	0.773
External test
	Intratumoral radiomics	0.673 (0.495-0.851)	0.568	0.556	0.579	0.556	0.579
	Peritumoral radiomics	0.605 (0.418-0.792)	0.568	0.389	0.737	0.560	0.583
	Combined radiomics	0.680 (0.498-0.862)	0.676	0.500	0.842	0.750	0.640

CI: Confidence interval; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Table 6 Diagnostic performance of the clinical-radiological feature, combined radiomics, and radiomics nomogram models

Set	Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
Training
	Clinical-radiological feature	0.833 (0.753-0.913)	0.792	0.830	0.754	0.737	0.776
	Combined radiomics	0.825 (0.747-0.903)	0.726	0.736	0.717	0.722	0.731
	Radiomics nomogram	0.859 (0.787-0.931)	0.792	0.830	0.754	0.772	0.816
Internal test
	Clinical-radiological feature	0.781 (0.644-0.918)	0.744	0.782	0.708	0.720	0.773
	Combined radiomics	0.788 (0.649-0.927)	0.745	0.783	0.709	0.720	0.773
	Radiomics nomogram	0.848 (0.726-0.970)	0.787	0.826	0.750	0.760	0.818
External test
	Clinical-radiological feature	0.684 (0.498-0.862)	0.676	0.500	0.842	0.750	0.64
	Combined radiomics	0.680 (0.502-0.866)	0.676	0.500	0.842	0.750	0.640
	Radiomics nomogram	0.757 (0.592-0.922)	0.729	0.611	0.842	0.750	0.783

CI: Confidence interval; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Citation: Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16(3): 857-874
URL: https://www.wjgnet.com/1948-5204/full/v16/i3/857.htm
DOI: https://dx.doi.org/10.4251/wjgo.v16.i3.857